Introduction

Calculate your house's estimated value

First, look up your house in Maryland’s
online database, and find out the "Enclosed square feet". Type that value into this form and hit calculate.

Enclosed square ft.

Like everybody else in the neighborhood, we got our property value assessment from the state and hit the ceiling. We filed an appeal and had our hearing last Saturday morning. As a part of it, I did some research on comparable houses. Since this may be useful to other people in the Town of Chevy Chase, I’ve posted an Excel spreadsheet that you can download with all of the arm’s-length house sales in 2004, along with some analysis.

While these data and analysis are directly applicable for people in the Town of Chevy Chase, if you live in nearby locales such as the Village of Chevy Chase or Section 3 or Section 5 you may also find it useful. I would not use it if you live further away than that, although you may find the research techniques may help you out.

Regression Analysis

Some interesting results came out of a regression analysis of the raw data. I tried fitting the sales prices against the square footage of the lot, the enclosed square footage of the structure, the subdivision, the number of bedrooms, the number of bathrooms, the number of half baths, a time trend variable, and a constant. The only significant variables were the enclosed square footage of the structure and the constant.

The existance of a buildable lot in the Town is worth approximately $311,000 regardless of its square footage.

A house is worth approximately $348 per square foot.

House prices have not risen significantly over the past year (although the average sale price may have gone up since many of the new houses being built are larger than the old houses that they are replacing).

The number of bedrooms and bathrooms is not a significant factor in the price of a house. For houses with the same square footage, four large bedrooms are worth the same amount as six small bedrooms.

The final regression anaylsis that includes only a constant and the enclosed area has an R
2of 0.71, which is rather high for a cross-sectional data set like this one. You can see from the scatter plot that the line fits the data remarkably well.

The Data

The house data come primarly from the online database maintained by the State of Maryland at
http://sdatcert3.resiusa.org/rp_rewrite/. I did searches in Montgomery County for property sales in District 7, Subdivisions 11, 13, 16, 18, 36, and 106 from January 1, 2004 through December 31, 2004. In addition, I cross-referenced this against searches of property sales between the same dates in map references HN32 and HN42, which cover the town and some surrounding areas. It turns out that there are some problems with the subdivision search features on the state’s website and some properties that show up in the map searches do not show up in the subdivision searches. In addition, there is one property that is listed as being in Subdivision 502, which is not anywhere near the town. Given its street address, I am including it anyway as I suspect that it is merely misclassified in the state’s database. Overall, I am reasonably certain that the data set covers all of the arm’s-length transactions in the town.

Once I located all of the sales, I pulled the actual data for each property on lot size, enclosed area, sale price, and subdivision from the same website, but using the Street Address search method. These were supplemented with data on the number of bedrooms, bathrooms, and half baths from various mailings by real estate agents based in the MRIS listings (Fairweather, Estridge, and W.C. & A.N. Miller [Cullinane]). Not all of the sold properties were included in the real estate agent reports, but there was a useful amount of coverage.